Smart motor data analytics with real-time algorithm

ABSTRACT

A computer-implemented method of Condition Monitoring (CM) for rotating machines like motors, a corresponding computer program, computer-readable medium and data processing system for CM for rotating machines as well as a system including the data processing system for CM for rotating machines. M accumulator variables are updated in real-time based on L samples including a current sample sn and at least one preceding sample Sn−1 of input data. Based on the M accumulator variables N spectral features are computed in real-time. A condition of the rotating machine is determined based on the N spectral features.

CROSS REFERENCE TO RELATED APPLICATIONS

This present patent document is a § 371 nationalization of PCTApplication Serial Number PCT/EP2020/078110 filed Oct. 7, 2020,designating the United States, which is hereby incorporated in itsentirety by reference. This patent document also claims the benefit ofEP19203532.7 filed on Oct. 16, 2019, which is hereby incorporated in itsentirety by reference.

FIELD

Embodiments relate to a computer-implemented method of ConditionMonitoring (CM) for rotating machines such as motors.

BACKGROUND

Failures of rotating machines (e.g., within bearings) for example bymisalignment or imbalance of components of the rotating machine oftenlead to unplanned downtimes. Therefore, in the field of ConditionMonitoring (CM), vibrational sensors that are attached to or alignednext to the rotating machines measure acceleration are used to derivemeasures for the state of the rotating machine as vibrations apply loadsto all components especially the bearings of the rotating machine.Failure types like broken bars of a rotor of rotating machines, failurecurrents to earth or between windings of a stator and/or rotor ofrotating machines, or eccentricities of rotating components of rotatingmachines are analysed by motor current signature analysis.

In CM, relevant damages may be determined using spectral information orrather spectral features of either the vibrational data or the currentdata measured at the rotating machine. Some features may be derived intime domain easily like e.g., the root mean square of velocity (compareto ISO10816) or acceleration levels or relative root mean square currentvalues of each phase to a sum of currents. Further features derived fromtime domain are e.g., Max, Min, Crest-factor, or the like. Yet thesetime domain features don't allow for conclusion upon a specific fault orroot cause. Therefore, experts regularly examine the condition ofrotating machines like motors by measuring the vibration and calculatinga velocity and envelope spectrum. Usually, CM diagnostics need a fixedrotational speed and/or load of the rotating machines for comparablediagnosis. The actual rotational speed is usually measured by arotational speed sensor or encoder. Spectra then may be normalizedtowards the actual rotational speed. For example, in identifying bearingdamage, imbalance, loose foots or other common fault state root causesthe amplitudes of rotational frequency harmonic peaks and sidebands areused as indicators for the specific failures and their severity.

All known literature and algorithms for CM applications usingvibrational sensors refer to acceleration and velocity spectracalculated via Fourier Transform (FT), for example via fast FT (FFT) andalso refer to Envelope-Spectra, i.e., the spectral decomposition of theenvelope of the raw signal as a mean of demodulation of impulseresonances of the localized bearing fault, being overrolled with eachturn. Motor Current Signal Analysis (MCSA) algorithms in the electricalcurrent domain use FFT and derive amplitudes at specific frequenciesthat are relied to slip, eccentricity, slot harmonics for applicationswith fixed rotational speed, like exemplarily in CM of induction motorsor variable speed drives.

MCSA Signals are based on temporal measurements of the current of theelectrical phases of a motor. Alternatively magnetic sensors may measurethe magnetic flux in or outside a motor that are correlated to thecurrents. A simultaneous measurement of the voltages in the electricalphases is advantageous for detection of failures of the electricalmachine with MCSA.

Traditional CM for diagnosis of fault conditions of rotating electricalmachines (e.g. electrical faults) and bearings include of anycombination of processing blocks from signal sampling, pre-processing,feature extraction, and classification. For expert systems at least thespectra are generated in a CM System to be used as the base of anexpert's decision. Another implementation depth may involve the outputof the CM quantities (or features) i.e., the amplitudes at frequenciescharacteristic of certain defects, fault states either of the drivetrain, an asset or the bearing, and the like. In more advanced CMsystems, a classification step at least decides based upon one spectralfeature which fault is present and in what severity level, so thatrecommendations for further steps may be produced.

Clearly with more output depth i.e., more blocks that are implemented ina CM system, the workload of the main processor unit raises.

For CM vibrational measurements and the calculation of the spectra thereis usually a high amount of signal bandwidth involved (e.g., samplingrates of 6.6 kHz [Kilohertz] to 48 kHz or even more for commonMicro-Electro-Mechanical System (MEMS) vibrational sensors likepiezo-electrical sensors using 16 bit data depth of each sample).Current and/or voltage sampling are performed typically in the lower kHzregion (e.g. 3200 Hz [Hertz] for a Simocode Datalogger, where typicallythree electrical phases are measured with 12 bit data depth, resultingin a data stream of 6×3.2 KHz @ 12 bit).

Measurement times of ca. 0.1 to 20 s [Second] are used and necessary forFFT, due to the frequency resolution of roundabout 0.1 to 1 Hz andlowest frequency of e.g. 1 to 10 Hz. Usually, three axis or electricalphases are sampled. Therefore, for calculation of the spectra a highamount of memory and thus a huge data storage is needed in hardwareintended for performing CM tasks, whilst the amount to time to performthe calculation of the spectra is not that important. High amount ofmemory, for example, from 51 kB [Kilobyte] (6600/s*16 bit*0.5 s) to 7 MB[Megabyte] (24000/s*16 bit*20 s) for each measurement axis, wherebysample length determines lowest possible frequency and resolution. Forrotating equipment a minimum of 5 to 10 rotations per measurement shouldbe taken to get stable amplitudes in the spectra.

The typical measurements in CM need several megabytes of high-resolutioninput data (e.g., vibrational data), mostly to be able to measure at the“lowest rotational speed” of an application. Therefore, the number ofsamples L needed is very high. For example, in hardware architectures ofvibrational CM systems the vibrational data sampled is either storedlocally and then communicated or streamed to a device or into a cloud,where the spectra and spectral features derived from them, are thencalculated. Therefore, all known solutions require high performanceCentral Processing Units (CPU) to perform the analysis. Microcontrollersfor calculation of spectral information close to sensor acquisitiondevices and on a low-cost base (IQ Connect), nowadays offer several 10MB to 100 MB of Random Access Memory (RAM). In common CM systems areduction in sample length or data acquisition rate or the amount ofaxes is possible and spectral information still may be calculated, butinevitably with reduced information output.

Consequently, in the known state of the art there is no low-costhardware described, for performing CM based on spectral features withsufficient spectral information.

The document EP 2 581 725 A2 discloses a system capable of automaticallydetecting a rolling-element bearing fault in a rotating machine. Thesystem receives, from at least one sensor, a sensor signal that includesat least one frequency and converts the sensor signal to a digitalvibration signal. The system modifies the vibration signal to generatean envelope signal and applies a transform to the enveloped signal togenerate an envelope spectrum. The system uses certain relationshipsamong envelope spectral line amplitudes and their harmonics to detectbearing faults. As such, the system detects a bearing fault withoutreference to predefined fault frequencies.

BRIEF SUMMARY AND DESCRIPTION

The scope of the present invention is defined solely by the appendedclaims and is not affected to any degree by the statements within thissummary. The present embodiments may obviate one or more of thedrawbacks or limitations in the related art.

Embodiments provide a computer-implemented method of ConditionMonitoring (CM) for rotating machines including the steps ofcontinuously receiving samples, updating in real-time M accumulatorvariables, computing in real-time N spectral features, and determining acondition. In the step of continuously receiving samples of input data,samples of input data based on at least one physical quantity over timeof a rotating machine are received. In the step of updating in real-timeM accumulator variables, M accumulator variables are updated inreal-time based on L samples. Thereby M is greater than or equal to one(M>=1). The L samples include a current sample s_(n) and at least onepreceding sample s_(n-1) of the input data. In the step of computing inreal-time N spectral features, N features are computed in real-timebased on the M accumulator variables and m supplemental variables.Thereby, N is greater than or equal to one (N>=1) and m is greater thanor equal to one (m>=1). In the step of determining a condition, acondition of the rotating machine is determined based on the N spectralfeatures. Further, the M accumulator variables are updated in real-timebased on the L samples and/or Lenv samples including the current samplesn and/or senv,n, a first preceding sample sn−1 and/or Senv,n−1 and asecond preceding sample sn−2 and/or senv,n−2 of the input data and/or ofthe envelope and the N spectral features are computed in real-time bythe Goertzel Algorithm, GA. Herein, the totality of the M accumulatorvariables is sufficient to determine the condition of the rotatingmachine. Further, the m supplemental variables are temporarily neededfor computing the N spectral features and the m supplemental variablesare not based on the received samples of the input data.

Embodiments provide a computer program includes instructions that, whenthe program is executed by a computer, cause the computer to carry outthe steps of the method.

Embodiments provide a computer-readable medium has stored thereon thecomputer program.

Embodiments provide a data processing system for CM for rotatingmachines includes an interface and computer. The interface is configuredto receive samples of input data based on at least one physical quantityover time of a rotating machine. The computer are configured to carryout the steps of the method.

Embodiments provide a system includes a rotating machine, at least onesensor and a data processing system. The at least one sensor isconfigured to measure at least one physical quantity over time of therotating machine. The data processing system is a data processingsystem. The data processing system is communicatively connected to theat least one sensor. The at least sensor is further configured toprovide the measured at least one physical quantity as samples of inputdata to the data processing system.

The rotating machine may be any rotating machine like a motor, agenerator, a turbine, and the like. For example, the rotating machinemay be an electrical motor including a stator and a rotor, where therotor may be pivoted via bearings in a housing of the electrical motor.The electrical motor is powered with electrical energy by an electricalcurrent, for example, an alternating current (AC) including an amperageand voltage with certain amplitudes and frequencies. The bearing may bea ball bearing, a cone bearing, a roller bearing, etc. and the bearingmay pivot the rotor at two points. The torque generated by theelectrical motor may be provided via a shaft of the electrical motorthat is fixedly connected to the pivoted rotor.

The at least one physical quantity of the rotating machine that isobserved over time may be any physical quantity that providesinformation about the condition or state of the rotating machine-likevibrations, the electrical current provided to the rotating machine(e.g., the electrical motor), or any other periodically occurringphysical quantity.

The at least one physical quantity is monitored by the at least onesensor. Thereto, the sensor measures directly or indirectly therespective physical quantity and converts the measurements into theinput data, for example into an (analogue) electrical amperage and/orvoltage that may be sampled and converted into a digital input signalwith a certain clock rate or sample frequency f_(s) by an analogue todigital (A/D) converter. The A/D converter may be part of the sensor orof the data processing unit. The (digital) input signal is provided inconsecutive samples at the certain clock rate, where each sample gives aquantitative value of the monitored physical quantity at a certain timepoint or rather interval. The clock rate, for example the interval, maybe predetermined or adjusted as needed.

The interface of the data processing system may be any communicativeinterface. The interface may be cable-based like a USB-interface, aCOM-interface, a RS32-interface etc. or wire-less like aBluetooth-interface, a ZigBee-interface, a WLAN-interface etc. Via theinterface the input data is provided sample by sample to the computer ofthe data processing system.

The input data is continuously received, for example at the interface ofthe data processing system. Thereby, the input data is received sampleby sample at the certain clock rate. Each received sample of the inputdata encodes a quantitative value of the respective at least onephysical quantity of the rotating machine.

The M accumulator variables are a set of predefined variables that areused to compute the N spectral features. The M accumulator variables arebased on L samples. The L samples include the current sample s_(n), thatwas last received, and the at least one preceding sample s_(n-1), thatwas received one time step of the clock rate before the current samples_(n). When more than one physical quantity is monitored, for exampletwo vibrations and one electrical current of the rotating machine, thenthe M accumulator variables are based on the L samples, where the Lsamples include the current samples s_(x,n) and at least one precedingsample s_(x,n-1) of each monitored physical quantity, for examples_(v1,n) and s_(v1,n-1) of the first monitored vibration, s_(v2,n) ands_(v2,n-1) of the second monitored vibration and s_(e,n) and s_(e,n-1)of the monitored electrical current. The M accumulator variables areupdated in real-time, such that the M accumulator variables are updatedbefore the next sample of the input data is received. After the Lsamples have been received, i.e. the current sample(s) s_((x,)n) and theat least one preceding sample(s) s_((x,)n-1) of the input data based onthe at least one (more than one) physical quantity measured at therotating machine, and the M accumulator variables are correspondinglyupdated, the M accumulator variables each hold a valid, stable,significant feature value, whereby the totality of the M accumulatorvariables is sufficient to determine the condition of the rotatingmachine.

After the M accumulator variables are updated, the N spectral featuresare computed based on the updated M accumulator variables and the msupplemental variables. Thereby, the N spectral features are computed inreal-time, such that the N spectral features are computed before thenext sample of the input data is received. The number of spectralfeatures N may be greater than the number of accumulator variables M(N>M) and for example, the number of spectral features N may besignificantly greater than the number of accumulator variables (N>>M,e.g., by one order of magnitude greater).

The m supplemental variables may be predefined like variables (e.g.,memory addresses) temporarily needed in computing the N spectralfeatures, but the m supplemental variables are not based on the receivedsamples of the input data. For example, the m supplemental variables aretemporarily needed for computing the N spectral features.

Based on the computed N spectral features, the condition of the rotatingmachine may be derived. The condition of the rotating machine may beautomatically derived, for example by a trained neural network, by adecision tree and the like.

Embodiments do not rely on classical approaches like FourierTransformation (FT) that is processing many samples of a long period oftime (e.g., 1 s [Second] or longer) in one calculation step andtherefore needs a large amount of memory for the many samples (e.g.,from about 51 kB to about 7 MB). Instead, embodiments avoid a largeamount of memory by processing every single data sample acquired fromthe rotating machine into the M accumulator variables in real-time andusing only the M accumulator variables together with the m supplementalvariables to establish the valid, stable significant N spectralfeatures, by applying a combination of real-time algorithms. Thus, thedata processing systems on which the N spectral features are computed inreal-time only need a very small amount of memory. This reduces the costof these data processing systems.

The M accumulator variables are updated in real-time based on the Lsamples and/or L_(env) samples including the current sample s_(n) and/ors_(env,n), a first preceding sample s_(n-1) and/or s_(env,n-1) and asecond preceding sample s_(n-2) and/or s_(env,n-2) of the input dataand/or of the envelope and the N spectral features are computed inreal-time by the Goertzel Algorithm (GA).

As real-time amplitude estimation algorithm the GA is used. Like theDiscrete Fourier Transform (DFT), GA analyses one selectable frequencycomponent from a discrete signal, here the samples of the input data.However, unlike the DFT the GA applies a single real-valued coefficientat each iteration, using real-valued arithmetic for real-valued inputsequences. For computing a small number of selected frequencycomponents, the GA is more numerically efficient than the DFT. Thesimple structure of the GA makes it well suited to small processors andembedded applications. A main calculation in the GA has the form of adigital filter operating on an input, here the current sample s_(n) in acascade of two stages with a parameter ω₀ giving the frequency underinvestigation (normalised to radians per sample). At a first stage ofthe GA an intermediate sequence sq_(n) (sq[n]) is computed:

sq[n]=s[n]+2 cos(ω₀)sq[n−1]−sq[n−2]

At the second stage, the following filter is applied to the intermediatesequence sq_(n) to produce the output, here the respective accumulatorvariable y_(n) (y[n]):

y[n]=sq[n]−e ^(−jω) ⁰ sq[n−1]

The first stage corresponds to a second order IIR filter with adirect-form structure, where its internal state variables equal the pastoutput values from that stage. Input values, here the current sampless_(n) for n smaller than zero (n<0) are presumed all equal to zero(s_(n<0)=0). To establish the initial filter state so that evaluationmay begin at the sample so, the filter states are assigned initialvalues for the intermediate state (s⁻²=s⁻¹=0). To avoid aliasinghazards, the frequency under investigation Wo may be restricted to therange of zero to 7C according to the Nyquist-Shannon sampling theorem(0<ω₀<=π).

The second-stage filter may be observed to be a FIR filter, since itscalculations do not use any of its past outputs.

In the following an implementation of the GA is given in pseudo code. Itmay be seen, that for a frequency under investigation ω₀ (“freq” inpseudo code) only the current sample s_(n) (“sample” in pseudo code) andthe two previous samples s_(n-1), s_(n-2) or rather the two previousintermediate sequences sq_(n-1), sq_(n-2) (“sprev”, “sprev2” in pseudocode) are needed as the M accumulator variables to update one of the Nspectral features, here the amplitude (“amplitude” in pseudo code) ofthe frequency under investigation ω₀ (“freq”) in the input data.Additionally, one coefficient (“coeff” in pseudo code), whichcoefficient may be pre-calculated in a table, in order to avoid atime-consuming cos-function call, and where coeff is a time independentweighting factor, simply, between actual and previous samples, thesampling frequency f_(s) (“fs” in pseudo code) and the frequency underinvestigation ω₀ (“freq”) are needed as supplemental variables besidesthe M accumulator variables.

def RTgoertzelFilter(sample, freq, fs, N):

global sprev;

global sprev2;

global totalpower;

normalizedfreq=freq/fs;

coeff=2*cos(2*π*normalizedfreq);

sample+=coeff*sprev−sprev2; 'first stage

sprev2=sprev;

sprev=sample;

power=sprev2*sprev2+sprev*sprev−coeff*sprev*sprev2; 'second stage

totalpower+=sample*sample;

amplitude=(power/totalpower)**0.5

return amplitude;

end def

Using multiple frequencies under investigation ω₀ one may either build aspectrum with any desired resolution or estimate exactly at thefrequencies that are e.g., harmonics of a rotational speed of therotating machine.

Considering the above it is clear, that the real-time GA only needs theM accumulators plus some additional minor m supplemental variables(i.e., coefficients, internal storage addresses etc.) to calculate theamplitudes at multiple frequencies under investigation ω₀. For example,ten rotational harmonic frequencies are typically investigated.

The same GA may also be applied for one or multiple frequencies underinvestigation to derive amplitudes in the envelope (based on the L_(env)samples of the envelope). The amplitudes for the one/multiplefrequencies under investigation are derived as spectral features forexample at the pass frequencies of an inner ring (f_(ir)), an outer ring(f_(or)), a cage (f_(cg)) and bearing balls (f_(bl)), their kth-higherharmonics (k_(xfir), k_(xfor), . . . ) and also sidebands (mostly atk*f_(ir)+/−p*f_(rot), with k, p∈(1, 10)) for a bearing of the rotatingmachine. When deriving the envelope in real-time, the amplitudes of thepass frequencies, their harmonics and sidebands may be determined usingthe real time GA as described before.

The GA is a fast algorithm that needs very little memory for calculatingthe N spectral features used in CM.

In an embodiment, the method further includes the step of derivingin-real time samples. In the step of deriving in-real time samples,samples of an envelope of the input data are derived in real-time basedon the samples of the input data, for example, by a rectificationfollowed by a lowpass filtering or by an asynchronous complex IQenvelope detector and, for example, by a biquad filter approach. The Maccumulator variables are additionally or alternatively updated based onL_(env) samples including a current sample n_(env) and at least onepreceding sample n_(env-1) of the envelope.

In CM for bearing fault detection the acceleration signals areinvestigated in an envelope spectrum. Reason being that the bearingfaults are introducing sequences of periodic spikes and get modulated bysome resonances in the system. Therefore, the envelope of the input datais needed. Only algorithms feasible to be calculated in real-time areuseful for building the envelope to ensure the overall real-timeprocessing. For example, a current sample of the envelope has to bederived from the current received sample before the next sample of theinput data is received.

A simple rectification followed by a lowpass filter may be implementedas a real-time algorithm for building the envelope of the input data.Such an asynchronous real square law envelope detector may be buildusing a real time lowpass filter (e.g., biquad filter approaches) aftersquaring of each sample n of the input data. For example, biquad filterapproaches are common in digital signal processing and the moststraightforward implementation is the so called “direct form 1”, thathas the following difference equation if normalized:

s _(env)[n]=b ₀ s[n]+b ₁ s[n−1]+b ₂ s[n−2]−a ₁ s[n−1]−a ₂ s[n−2]

where the coefficients b₀, b₁, b₂ determine zeros, and the coefficientsa₁, a₂, determine the position of the poles of the correspondingtransfer function composed of two quadratic polynomials. Here thecoefficients b₀, b₁, b₂, a₁, a₂ are included in the supplementalvariables m. (s_(n) corresponds to s[n] and s_(env,n) corresponds tos_(env)[n] and so forth).

Alternatively, also suitable is multiplication of the input data by sine(In phase—I) and cosine (Quadrature—Q), where w is the carrierfrequency. This is called an asynchronous complex IQ envelope detector,where a real time multiplication of the input data by sine and cosinecoefficients having (roundabout or exact) the same frequency ω as thecarrier frequency ω.

Both methods mentioned above may be calculated in real-time. In thealgorithms only the current sample s_(n) of the current time step n andtwo previous samples s_(n-1) and s_(n-2) of the time steps n−1 and n−2,as well as the pre-calculated coefficients are necessary to filter inreal-time.

The M accumulator variables are additionally or alternatively updatedbased on the L_(env) samples that include the current samples_(env,n)(s_(env)[n]) and the at least one preceding samples_(env,n-1)(s_(env)[n−1]) of the envelope. Consequently, the N spectralfeatures are computed based on the M accumulator variables that areadditionally or alternatively based on the L_(env) samples of theenvelope.

The real-time deriving of the envelope of the input data enablesextraction of further information for CM, for example for bearing faultdetection, while only a small amount of memory needed for the L_(env)samples of the envelope.

In an embodiment, the input data includes vibrational data based on avibration over time of the rotating machine and/or electrical data basedon an electrical current and/or voltage over time provided to therotating machine.

In an embodiment, the interface includes a first interface and/or asecond interface. The first interface is configured to receive samplesof vibrational data based on a vibration over time of the rotatingmachine. The second interface is configured to receive samples ofelectrical data based on an electrical current over time provided to therotating machine.

According to a further refinement the at least one sensor is avibrational sensor and additionally or alternatively an electricalsensor. The vibrational sensor is configured to measure a vibration overtime of the rotating machine and to provide the measured vibration assamples of vibrational data to the data processing system. Theelectrical sensor is configured to measure an electrical current(amperage) and/or voltage and/or magnetic field over time provided tothe rotating machine and to provide the measured electrical current(amperage) and/or voltage as samples of electrical data to the dataprocessing system.

For example, vibrations of the rotating machine may be measured by thevibrational sensor that may be based on the Piezo-electric effect.Thereby, the vibrations are converted into a voltage that is generateddue to the vibrations by the vibrational sensor. The amplitude andfrequency of the generated voltage resemble the vibrations measured atthe rotating machine. The continuous (analogue) periodical voltage maythen be sampled into consecutive samples with the certain clock rate orsampling frequency f_(s) by an A/D converter. Each sample gives anamplitude of the voltage at the respective time point.

The samples provided by the vibrational sensor are received at the firstinterface of the data processing system and forwarded to the computer.

For example, the electrical current provided to the rotating machine(e.g., electrical motor) may be measured by the electrical sensor thatmay be an electrical current (amperage) sensor or a voltage detector.Thereby, the time course of the provided electrical current is convertedinto a proportional signal that is generated due to the amplitude of theelectrical current by the electrical sensor (e.g. a Hall effect sensor,a transformer/current clamp meter, a fluxgate transformer type sensor, aresistor, whose voltage is directly proportional to the current throughit, a fibre optic current sensor, using an interferometer to measure thephase change in the light produced by a magnetic field, a Rogowski coil,etc.). The amplitude and frequency of the generated signal (voltage)resemble the electrical current measured at the rotating machine. Thecontinuous (analogue) periodical signal may then be sampled intoconsecutive samples with the certain clock rate or sampling frequencyf_(s) by an A/D converter. Each sample gives an amplitude of the signalat the respective time point.

The samples provided by the electrical sensor are received at the secondinterface of the data processing system and forwarded to the computer.

For example, in CM the following types of condition/state/failure andthe spectral features used to identify them are given:

unbalance rotation frequency f_(n) as RMS [Root Mean Square]

misalignment/ single f_(n) as RMS/

coupling defect double f_(n) as RMS

mounting defect single f_(n) as RMS/

double f_(n) as RMS/

triple f_(n) as RMS

blade passing frequency f_(SP) as RMS

(e.g. of turbine)

meshing defect f_(Z)

(e.g. of gear)

belt defect f_(R) as RMS

resonance resonance frequency=f_(n) as RMS

bearing wear f_(LE) as DKW (Diagnosekennwert, german for diagnosischaracteristic value, a value with respect to a historical recordedstate)

bearing damage frequency envelope curve, geometry dependent for outerring inner ring, cage and rolling element of bearing as DKW

electrical stator fault double line frequency f_(line) as RMS

electrical rotor fault f_(bar) as RMS

rotor bar break f_(line) and modulation with slip frequency f_(slip) asRMS

With the vibrational data and/or the electrical data as input data, manydifferent conditions of the rotating machine may be reliably determined.

In an embodiment the method further includes the step continuouslyreceiving at least one characteristic rotational speed or determining inreal-time the at least one characteristic rotational speed. In the stepof continuously receiving at least one characteristic rotational speed,at least one characteristic rotational speed of the rotating machine iscontinuously received. In the step of determining in real-time the atleast one characteristic rotational speed, the at least onecharacteristic rotational speed of the rotating machine is determined inreal-time based on the vibrational data by a real-time Frequency LockedLoop, FLL, method. The M accumulator variables are updated basedadditionally on the rotational speed or harmonics thereof.

The characteristic rotational speed of the rotating machine may bemeasured by a rotational speed sensor at the rotating machine. Themeasured characteristic rotational speed may be provided to the dataprocessing system at a third interface from where it is forwarded to thecomputer.

Alternatively, or additionally, the real-time FLL may be used incombination with the vibrational data, for estimating the most correctfrequency as characteristic rotational speed of the rotating machine. Acorresponding FFL-analyser includes an oscillator, a mixer, and ananalysing block. The oscillator generates a digital oscillating signalS′ having an oscillating frequency f′. The mixer is provided with thedigital oscillating signal S′ and the samples of the vibrational dataand generates a mixed signal S″ therefrom. The mixed signal S″ includesa first signal part of a sum signal of the frequency of the vibrationalsignal and the oscillating frequency f′ as well as a second signal partof a difference signal of the frequency of the vibrational signal andthe oscillating frequency f′. The analysing block updates theoscillating frequency f′ based on the second signal part including thedifference of the frequency of the vibrational signal and theoscillating frequency f′ such that the oscillating frequency f′ isadjusted to the frequency of the vibrational signal, i.e., FLL(frequency locked loop).

Additionally, to the characteristic rotational speed, harmonics of thecharacteristic rotational speed may be calculated and used as furtheraccumulator variables.

The measured or determined (estimated) characteristic rotational speedmay be used as the frequency under investigation ω₀ or as one of thefrequencies under investigation ω₀, to ensure that also for higherharmonics, where an error in e.g., the f_(rot) estimation or measurementis higher with higher k (also valid for p for the sideband calculation)is corrected and the amplitude is determined (e.g. via the GA) at thebest frequency approximate possible.

In an embodiment the N spectral features include at least one amplitudeof at least one first frequency under investigation ω₀, when the Maccumulator variables include the at least one first frequency underinvestigation ω₀ and/or at least one amplitude of at least one secondfrequency under investigation ω_(0,env) in the envelope, when the Maccumulator variables include the at least one second frequency underinvestigation ω_(0,env).

For example, based on the GA, the amplitude of the at least one firstfrequency under investigation ω₀ may be calculated as one of the Nspectral features of the input data (e.g., vibrational data and/orelectrical data) in real-time. Also the amplitude of the at least onesecond frequency under investigation ω_(0,env) may be calculated as oneof the N spectral features of the envelope (e.g. envelope of thevibrational data and/or electrical data) in real-time.

Embodiments and its technical field are subsequently explained infurther detail by exemplary embodiments shown in the drawings. Theexemplary embodiments only conduce better understanding and in no caseare to be construed as limiting for the scope of the describedembodiments. For example, it is possible to extract aspects of thesubject-matter described in the figures and to combine it with othercomponents and findings of the present description or figures, if notexplicitly described differently. Equal reference signs refer to thesame objects, such that explanations from other figures may besupplementally used.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts a schematic flow chart of an embodiment of thecomputer-implemented method of CM for rotating machines.

FIG. 2 depicts a schematic flow chart of a further embodiment of thecomputer-implemented method of CM for rotating machines.

FIG. 3 depicts a schematic view of an embodiment of thecomputer-readable medium.

FIG. 4 depicts a schematic view of the data processing system for CM forrotating machines according to an embodiment.

FIG. 5 depicts a schematic view of an embodiment of the system.

FIG. 6 depicts a schematic block diagram of an asynchronous real squarelaw envelope detector according to an embodiment.

FIG. 7 depicts a schematic block diagram of an asynchronous complex IQenvelope detector according to an embodiment.

FIG. 8 depicts two diagrams comparing the output of a FFT with theoutput of the GA according to an embodiment.

DETAILED DESCRIPTION

In FIG. 1 an embodiment of the computer-implemented method of CM forrotating machines is schematically depicted. The method includes thesteps continuously receiving S1 samples of input data, continuouslyreceiving S1 a at least one characteristic rotational speed, derivingS1′ in-real time samples of an envelope, updating S2 in real-time Maccumulator variables, computing S3 in real-time N spectral features anddetermining S4 a condition.

In the step of continuously receiving S1 samples of input data, samplesof input data, for example of vibrational data and electrical data, arecontinuously received with a predetermined sampling frequency f_(s). Theinput data is based on two physical quantities, for example thevibrational data is based on a vibration at a bearing of a rotatingmachine and the electrical data is based on an electrical currentprovided to the electrical machine. The input data is provided byrespective sensors, for example the vibrational data is provided by avibrational sensor measuring the vibrations at the bearing of therotating machine the electrical data is provided by an electrical sensormeasuring the electrical current provided to the electrical machine. Theinput data, i.e., the vibrational data and electrical data are eachreceived as consecutive samples, hence as digital signals.

In the step of continuously receiving S1 a at least one characteristicrotational speed, a characteristic rotational speed of the rotatingmachine is continuously received with the predetermined samplingfrequency f_(s). The characteristic rotational speed is provided by arotational speed sensor measuring the current rotational speed of ashaft of the rotating machine. The characteristic rotational speed isreceived as digital signal, i.e., in consecutive samples.

In the step of deriving S1′ in-real time samples of an envelope, samplesof an envelope are derived from the samples of the input data, i.e.samples of an envelope of the vibrational data and samples of anenvelope of the electrical data are derived. The samples of the twoenvelopes are derived either by an asynchronous real square law envelopedetector 30 (cf. FIG. 6) or by an asynchronous complex IQ envelopedetector 40 (cf. FIG. 7).

The steps of updating S2 in real-time M accumulator variables andcomputing S3 in real-time N spectral features are based on the GoertzelAlgorithm (GA).

In the step of updating S2 in real-time M accumulator variables, Maccumulator variables are updated in real-time based on L samples. Here,M=8 accumulator variables include the current samples of the input data,for example of the vibrational data and the electrical data as well asthe current samples of the envelopes of the vibrational data and theelectrical data. Further, the M accumulator variables include twoprevious intermediate sequences sq_(n-1), sq_(n-2) of the vibrationaldata and the electrical, data as well as of the envelopes of thevibrational data and the electrical data. For example, the M accumulatorvariables correspond to the L samples. The L samples include a currentsample of the vibrational data s_(v,n) and a current sample of theelectrical data s_(e,n) as well as a current sample of the envelope ofthe vibrational data s_(v,env,n) and a current sample of the envelope ofthe electrical data s_(e,env,n). Further, the L samples include a firstpreceding sample of the vibrational data s_(v,n-1) and a first precedingsample of the electrical data s_(e,n-1) as well as a first precedingsample of the envelope of the vibrational data s_(v,env,n-1) and a firstpreceding sample of the envelope of the electrical data s_(e,env,n-1).Additionally, the L samples include a second preceding sample of thevibrational data s_(v,n-2) and a second preceding sample of theelectrical data s_(e,n-2) as well as a second preceding sample of theenvelope of the vibrational data s_(v,env,n-2) and a second precedingsample of the envelope of the electrical data s_(e,env,n-2). Here, thefirst and second preceding samples s_(v,n-1), s_(e,n-1), s_(v,env,n-1),s_(e,env,n-1), s_(v,n-2), s_(e,n-2), s_(v,env,n-2), s_(e,env,n-2) arereplaced by the corresponding intermediate sequences sq_(v,n-1),sq_(e,n-1), sq_(v,env,n-1), sq_(e,env,n-1), sq_(v,n-2), sq_(e,n-2),sq_(v,env,n-2), sq_(e,env,n-2) in the M accumulator variables, where thefirst and second intermediate sequences have been calculated based onthe respective first and second preceding samples.

In the step of computing S3 in real-time N spectral features, the Nspectral features are computed in real-time based on the M=8 accumulatorvariables. Here N=40 amplitudes of ten first frequencies underinvestigation ω_(0,v,1) to ω_(0,v,10) in the vibrational data, ten firstfrequencies under investigation ω_(0,e,1) to ω_(0,e,10) in theelectrical data, ten second frequencies under investigationω_(0,env,v,1) to ω_(0,env,v,10) in the envelope of the vibrational dataand ten second frequencies under investigation ω_(0,env,v,1) toω_(0,env,v,10) in the envelope of the vibrational data are computed inreal-time with the GA.

In the step of determining S4 a condition, a condition of the rotatingmachine is derived based on the N=40 calculated amplitudes.

In FIG. 2 a further embodiment of the computer-implemented method of CMfor rotating machines is schematically depicted. The method includes thesame steps like the method of FIG. 1 except that instead of the step S1a the step of determining S1 b in real-time the at least onecharacteristic rotational speed is included by the method of FIG. 2.Therefore, only the difference between the two embodiments of FIG. 1 andFIG. 2, for example the step S1 b, is described in the following.

In the step of determining S1 b in real-time the at least onecharacteristic rotational speed, the at least one characteristicrotational speed of the rotating machine is determined in real-timebased on the vibrational data by a real-time Frequency Locked Loop (FLL)method. For example, a FFL-analyser (not depicted) is used fordetermining the characteristic rotational speed in real-time. TheFFL-analyser includes an oscillator, a mixer and an analysing block. Theoscillator generates a digital oscillating signal S′ having anoscillating frequency f′. The mixer is provided with the digitaloscillating signal S′ and the samples of the vibrational data andgenerates a mixed signal S″ therefrom. The mixed signal S″ includes afirst signal part of a sum signal of the frequency of the vibrationalsignal and the oscillating frequency f′ as well as a second signal partof a difference signal of the frequency of the vibrational signal andthe oscillating frequency f′. The analysing block updates theoscillating frequency f′ based on the second signal part including thedifference of the frequency of the vibrational signal and theoscillating frequency f′ such that the oscillating frequency f′ isadjusted to the frequency of the vibrational signal.

The program steps of the computer program correspond to the method stepsof the method described above and as depicted in FIGS. 1 to 2.

In FIG. 3 an embodiment of the computer-readable medium 1 isschematically depicted.

Here, a computer-readable storage disc 1 like a Compact Disc (CD),Digital Video Disc (DVD), High Definition DVD (HD DVD) or Blu-ray Disc(BD) has stored there on the computer program and as schematically shownin FIGS. 1 to 2. However, the computer-readable medium may also be adata storage like a magnetic storage/memory (e.g. magnetic-core memory,magnetic tape, magnetic card, magnet strip, magnet bubble storage, drumstorage, hard disc drive, floppy disc or removable storage), an opticalstorage/memory (e.g. holographic memory, optical tape, Tesa tape,Laserdisc, Phasewriter (Phasewriter Dual, PD) or Ultra Density Optical(UDO)), a magneto-optical storage/memory (e.g. MiniDisc orMagneto-Optical Disk (MO-Disk)), a volatile semiconductor/solid statememory (e.g. Random Access Memory (RAM), Dynamic RAM (DRAM) or StaticRAM (SRAM)), a non-volatile semiconductor/solid state memory (e.g. ReadOnly Memory (ROM), Programmable ROM (PROM), Erasable PROM (EPROM),Electrically EPROM (EEPROM), Flash-EEPROM (e.g. USB-Stick),Ferroelectric RAM (FRAM), Magnetoresistive RAM (MRAM) or Phase-changeRAM).

In FIG. 4 an embodiment of the data processing system 10 isschematically depicted.

The data processing system 10 may be a personal computer (PC), a laptop,a tablet, a server, a distributed system (e.g., cloud system) and thelike. The data processing system 10 includes a central processing unit(CPU) 11, a memory having a random-access memory (RAM) 12 and anon-volatile memory (MEM, e.g., hard disk) 13, a human interface device(HID, e.g., keyboard, mouse, touchscreen etc.) 14 and an output device(MON, e.g., monitor, printer, speaker, etc.) 15. Further, the dataprocessing system 10 includes a first interface 16 a, a second interface16 b and a third interface 16 c. The CPU 11, RAM 12, HID 14, MON 15 andthe three interfaces 16 a, 16 b, 16 c are communicatively connected viaa data bus. The RAM 12 and MEM 13 are communicatively connected viaanother data bus. The computer program schematically depicted in FIGS. 1to 2 may be loaded into the RAM 12 from the MEM 13 or anothercomputer-readable medium 1. According to the computer program thevibrational data from the vibrational sensor is received at the firstinterface 16 a and the electrical data from the electrical sensor isreceived at the second interface 16 b. At the third interface 16 c thecharacteristic rotational speed of the rotating machine is received fromthe rotational speed sensor. Further, the CPU 11 executes the steps ofthe computer-implemented method and as schematically depicted in FIGS. 1to 2. The execution may be initiated and controlled by a user via theHID 14. The status and/or result of the executed computer program may beindicated to the user by the MON 15. The result of the executed computerprogram may be permanently stored on the non-volatile MEM 13 or anothercomputer-readable medium.

The HID 14 and MON 15 for controlling execution of the computer programmay be included by a different data processing system like a terminalcommunicatively connected to the data processing system 10 (e.g., cloudsystem).

In FIG. 5 an embodiment of the system is schematically depicted. Thesystem 20 includes a rotating machine, here an electrical motor 21, avibrational sensor 22 a, an electrical sensor 22 b, a rotational speedsensor 22 c and the data processing system 10 and as depicted in FIG. 4.

The electrical motor 21 includes a stator and a rotor with a fixedlyattached shaft. The rotor with the shaft is pivoted at two points bybearings. The electrical motor 21 converts electrical energy provided aselectrical current into kinetic energy in form of a rotation with acertain rotational speed and torque.

The vibrational sensor 22 a is arranged near one of the bearings of theelectrical motor 21 and communicatively connected to the first interface16 a of the data processing system 10. For converting vibrations intovibrational data the piezo-electric effect or MEMS sensors based onsilicon may be used. The vibrational sensor 22 a measures vibrations ofthe one of the two bearings and provides the corresponding vibrationaldata to the first interface 16 a.

The electrical sensor 22 b is arranged at the electrical motor 21 andcommunicatively connected to the second interface 16 b of the dataprocessing system 10. The electrical sensor 22 b measures the electricalcurrent provided to the electrical motor 21 via a resistor and providesthe corresponding electrical data to the second interface 16 b.

The rotational speed sensor 22 c is arranged at the shaft of theelectrical motor 21 and communicatively connected to the third interface16 c of the data processing system 10. The rotational speed sensor 22 cmeasures the current rotational speed of the shaft of the electricalmotor 21 and provides the corresponding characteristic rotational speedto the third interface 16 c.

Alternative setups use either vibrational sensor 22 a or electricalsensor 22 b. The rotational speed sensor 22 c may be omitted in caseswhere the rotational speed may be derived from the input data providedby the vibrational sensor 22 a and/or the electrical sensor 22 b usingalgorithms or methods like FLL.

The provided vibrational data, electrical data and characteristicrotational speed is forwarded to the processor, for example the RAM 12and the CPU 11, for executing the steps of the method and as depicted inFIGS. 1 and 2.

In FIG. 6 an asynchronous real square law envelope detector 30 isschematically depicted. The envelope detector 30 includes a squaringunit 32, a lowpass filter 34 and a square root unit 35.

The current samples of the input data 31, here of the vibrational dataand the electrical data, are first squared in the squaring unit 32, thatresults in the squared input data 33, and then lowpass-filtered by thelowpass filter 34. The output of the low pass filter 34 is provided tothe square root unit 35, where the square root is taken. The output isthe envelope 36 of the input data, here of the vibrational data and theelectrical data.

In FIG. 7 an asynchronous complex IQ envelope detector 40 isschematically depicted. The envelope detector 40 includes a quadraturepower divider 41.

The input data, the vibrational data and the electrical data, ismultiplied by sine (In phase—I) and cosine (Quadrature—Q), where w isthe carrier frequency.

In FIG. 8 two diagrams comparing the output of a FFT with the output ofthe GA are schematically depicted. In the left diagram, a FFT spectrum(crosses) and real-time GA amplitudes for 65 frequencies (dots) aredepicted. In the right diagram, a FFT spectrum (solid line) andreal-time GA amplitudes for 7 the characteristic rotational speed andseven harmonics (dots) are depicted. Thus it is clear, that thereal-time GA only needs the M accumulator variables (plus some madditional supplementary variables for e.g. coefficients, internalstorage) to calculate the amplitudes at multiple frequencies underinvestigation. The few (M+m) variables needed for the GA may be storedin a small memory. Such small memory very cheap compared to largememories needed for the variables for FFT.

Although specific embodiments have been illustrated and describedherein, it will be appreciated by those of ordinary skill in the artthat a variety of alternate and/or equivalent implementations exist. Itshould be appreciated that the embodiment or embodiments are onlyexamples, and are not intended to limit the scope, applicability, orconfiguration in any way. Rather, the foregoing summary and detaileddescription will provide those skilled in the art with a convenient roadmap for implementing at least one y embodiment, it being understood thatvarious changes may be made in the function and arrangement of elementsdescribed in an embodiment without departing from the scope as set forthin the appended claims and their legal equivalents. Generally, thisapplication is intended to cover any adaptations or variations of thespecific embodiments discussed herein.

In the foregoing detailed description, various features are groupedtogether in one or more examples for the purpose of streamlining thedisclosure. It is understood that the above description is intended tobe illustrative, and not restrictive. It is intended to cover allalternatives, modifications and equivalents as may be included withinthe scope of the invention. Many other examples will be apparent to oneskilled in the art upon reviewing the above specification.

Specific nomenclature used in the foregoing specification is used toprovide a thorough understanding of the invention. However, it will beapparent to one skilled in the art in light of the specificationprovided herein that the specific details are not required in order topractice the invention. Thus, the foregoing descriptions of specificembodiments of the present invention are presented for purposes ofillustration and description. They are not intended to be exhaustive orto limit the invention to the precise forms disclosed; obviously manymodifications and variations are possible in view of the aboveteachings. The embodiments were chosen and described in order to bestexplain the principles of the invention and its practical applications,to thereby enable others skilled in the art to best utilize theinvention and various embodiments with various modifications as aresuited to the particular use contemplated. Throughout the specification,the terms “including” and “in which” are used as the plain-Englishequivalents of the respective terms “including” and “wherein,”respectively. Moreover, the terms “first,” “second,” and “third,” etc.,are used merely as labels, and are not intended to impose numericalrequirements on or to establish a certain ranking of importance of theirobjects. In the context of the present description and claims theconjunction “or” is to be understood as including (“and/or”) and notexclusive (“either . . . or”).

1. A Computer-implemented method of Condition Monitoring for rotating machines, the method comprising: continuously receiving samples of input data based on at least one physical quantity over time of a rotating machine; updating in real-time M accumulator variables, where M>=1, based on L samples including a current sample sn and at least one preceding sample Sn−1 of the input data; computing in real-time N spectral features, where N>=1, based on the M accumulator variables and m supplemental variables, where m>=1; and determining a condition of the rotating machine based on the N spectral features; wherein the M accumulator variables are updated in real-time based on the L samples or Lenv samples including the current sample sn or senv,n, a first preceding sample sn−1 or Senv,n−1 and a second preceding sample sn−2 or senv,n−2 of the input data or of an envelope and the N spectral features are computed in real-time by a Goertzel Algorithm; wherein the totality of the M accumulator variables is sufficient to determine the condition of the rotating machine, and wherein the m supplemental variables are temporarily needed for computing the N spectral features and the m supplemental variables are not based on the received samples of the input data.
 2. The Method of claim 1, further comprising: deriving in-real time samples of an envelope of the input data based on the samples of the input data by a rectification followed by a low-pass filtering or by an asynchronous complex IQ envelope detector, or by an biquad filter approach, wherein the M accumulator variables are additionally or alternatively updated based on L_(env) samples including a current sample senv,n and at least one preceding sample senv,n−1 of the envelope.
 3. The Method of claim 1, wherein the input data includes vibrational data based on a vibration over time of the rotating machine or electrical data based on an electrical current or voltage over time provided to the rotating machine.
 4. The Method of claim 3, further comprising: continuously receiving at least one characteristic rotational speed of the rotating machine; or determining in real-time the at least one characteristic rotational speed of the rotating machine based on the vibrational data by a real-time Frequency Locked Loop method, wherein the M accumulator variables are updated based additionally on the rotational speed or harmonics thereof.
 5. The Method of claim 2, wherein the N spectral features include at least one amplitude of at least one first frequency under investigation in the input data, when the M accumulator variables include the at least one first frequency under investigation, or at least one amplitude of at least one second frequency under investigation in the envelope, when the M accumulator variables include the at least one second frequency under investigation.
 6. A Computer program comprising instructions which, when the program is executed by a computer, cause the computer to: continuously receive samples of input data based on at least one physical quantity over time of a rotating machine; update in real-time M accumulator variables, where M>=1, based on L samples including a current sample sn and at least one preceding sample Sn−1 of the input data; compute in real-time N spectral features, where N>=1, based on the M accumulator variables and m supplemental variables, where m>=1; and determine a condition of the rotating machine based on the N spectral features; wherein the M accumulator variables are updated in real-time based on the L samples or Lenv samples including the current sample sn or senv,n, a first preceding sample sn−1 or Senv,n−1 and a second preceding sample sn−2 or senv,n−2 of the input data or of an envelope and the N spectral features are computed in real-time by a Goertzel Algorithm; wherein the totality of the M accumulator variables is sufficient to determine the condition of the rotating machine, and wherein the m supplemental variables are temporarily needed for computing the N spectral features and the m supplemental variables are not based on the received samples of the input data.
 7. (canceled)
 8. (canceled)
 9. (canceled)
 10. (canceled)
 11. A System comprising: a rotating machine; at least one sensor configured to measure at least one physical quantity over time of the rotating machine (21); and a data processing system communicatively connected to the at least one sensor, the data processing system comprising: an interface configured to receive samples of input data based on at least one physical quantity over time of the rotating machine; and a processor configured to: continuously receive samples of input data based on at least one physical quantity over time of a rotating machine; update in real-time M accumulator variables, where M>=1, based on L samples including a current sample sn and at least one preceding sample Sn−1 of the input data; compute in real-time N spectral features, where N>=1, based on the M accumulator variables and m supplemental variables, where m>=1; and determine a condition of the rotating machine based on the N spectral features: wherein the M accumulator variables are updated in real-time based on the L samples or L_(env) samples including the current sample sn or a senv,n, a first preceding sample sn−1 or Senv,n−1 and a second preceding sample sn−2 or senv,n−2 of the input data or of an envelope and the N spectral features are computed in real-time by a Goertzel Algorithm; wherein the totality of the M accumulator variables is sufficient to determine the condition of the rotating machine, and wherein the m supplemental variables are temporarily needed for computing the N spectral features and the m supplemental variables are not based on the received samples of the input data; wherein the at least sensor is further configured to provide the measured at least one physical quantity as samples of input data to the data processing system.
 12. The System of claim 11, wherein the at least one sensor comprises at least one of: a vibrational sensor configured to measure a vibration over time of the rotating machine and to provide the measured vibration as samples of vibrational data to the data processing system, or an electrical sensor configured to measure an electrical current voltage, or magnetic field over time provided to the rotating machine and to provide the measured electrical current or voltage as samples of electrical data to the data processing system.
 13. The System of claim 12, wherein the processor of the data processing system is further configured to: continuously receive at least one characteristic rotational speed of the rotating machine; or determine in real-time the at least one characteristic rotational speed of the rotating machine based on the vibrational data by a real-time Frequency Locked Loop method, wherein the M accumulator variables are updated based additionally on the rotational speed or harmonics thereof. 